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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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] | open | false | null | [] | null | 3 | "2021-05-17T15:31:34Z" | "2021-08-20T05:31:49Z" | null | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
# S3 (REQUIRES s3fs)
json_df = pd.read_json("s3://public-r-data/ghcran.json")
# URL (NO REQUIREMENT)
json_df = pd.read_json("http://public-r-data.s3-website-us-east-1.amazonaws.com/ghcran.json")
json_df.loc[884, "Title"]
# Misc Functions of Eduard Sz<c3><b6>cs
json_df.loc[213, "Author"]
# Kirill M<c3><bc>ller [aut, cre]
json_df.loc[336, "Maintainer"]
# H<c3><a9>l<c3><a8>ne Morlon <[email protected]>
```
#### Problem description
All UTF-8 encoding should render to their true character form and not hex code representation. Users need reliability in text value consistency from json format to pandas containers. Looking at some source code, IO JSON appears to use the `ujson` C extension which may be causing above behavior. I posted [same question](https://stackoverflow.com/q/67561276/1422451) on StackOverflow with an interesting workaround answer.
For reference see https://www.utf8-chartable.de/ for table of hex codes and corresponding characters. For c2-c9 codes, use drop down values: `U+0000 ... U+007F: Basic Latin`, `U+0080 ... U+00FF: Latin-1 Supplement`, `U+0100 ... U+017F: Latin Extended-A`, `U+0180 ... U+024F: Latin Extended-B`.
#### Expected Output
All angle bracketed hex codes rendered to true accent or diatric characters.
```python
json_df.loc[884, "Title"]
# Misc Functions of Eduard Szöcs
json_df.loc[213, "Author"]
# Kirill Müller [aut, cre]
json_df.loc[336, "Maintainer"]
# Hélène Morlon <[email protected]>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : fa3dbc117f58fdcb615144529d17c5dbfcdcb082
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-50-generic
Version : #56~20.04.1-Ubuntu SMP Mon Apr 12 21:46:35 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+1672.gfa3dbc117f
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 49.6.0.post20210108
Cython : 0.29.23
pytest : 6.2.3
hypothesis : 6.10.1
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : 1.4.0
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.04.0
fastparquet : 0.5.0
gcsfs : 2021.04.0
matplotlib : 3.4.1
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : 2021.04.0
scipy : 1.6.3
sqlalchemy : 1.4.12
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.17.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df =pd.DataFrame({'year':[2005, 2006], 'cusip':['111', '222']})
df_comp = pd.DataFrame({'year':[2005, 2006], 'cusip':['111', '222'], 'test': [5, 10]})
df_comp['year'] = df_comp['year'].astype(int)
df = df.merge(df_comp, how='left', on=['cusip', 'year'], indicator=True, suffixes=[None, '_wyear'])
print(df['_merge'].value_counts())
df = df.drop(columns='_merge')
df_comp2 = df_comp.drop(columns='year').drop_duplicates()
df = df.merge(df_comp2, how='left', on=['cusip'], indicator=True, suffixes=[None, '_woyear'])
print(df['_merge'].value_counts())
df = df.drop(columns='_merge')
print(df.columns)
```
Index(['year', 'cusip', 'test', 'test_woyear'], dtype='object')
#### Problem description
The suffixes parameter of _merge only produces a suffix for the second merge not the first. This behavior persists if I switch the two merges.
#### Expected Output
Index(['year', 'cusip', 'test_wyear', 'test_woyear'], dtype='object')
#### Output of ``pd.show_versions()``
<details>
C:\Users\Hiwi_Tower\AppData\Local\Programs\Python\Python38\python.exe "W:/Lehrstuhlverzeichnis/300_Forschung/310_Aktuelle_Projekte/Paper Hauke/Python/Digital/getdigitalanalysts.py"
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.UTF-8
pandas : 1.2.4
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.4
setuptools : 41.2.0
Cython : 0.29.14
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 9 | "2021-05-17T17:47:23Z" | "2021-05-26T06:04:51Z" | "2021-05-26T01:47:34Z" | CONTRIBUTOR | null | - xref #41486
- generic.where. tests added / passed
- Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them.
- whatsnew entry. -- added. | {
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} | 4 | "2021-05-17T19:15:42Z" | "2021-05-19T08:43:22Z" | "2021-05-18T12:50:37Z" | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/41511#discussion_r633386611
as request, ping @jreback @simonjayhawkins
I've removed the ability to pass an integer to `allowed_arguments` as it added complexity, was only used once in the codebase anyway, and wasn't even covered by tests
Now, if `version` is None, then it'll just print "In a future version of pandas", it'll show the class name (e.g. `Series.drop_duplicates` instead of just `drop_duplicates`) and won't print `'self'` in the warning message | {
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} | 4 | "2021-05-17T20:22:52Z" | "2021-05-18T22:27:21Z" | "2021-05-18T22:23:22Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
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Also adds a convert_period option to lib.maybe_infer_objects
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- [ ] tests added / passed
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See #34213.
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 0 | "2021-05-18T10:58:59Z" | "2021-05-18T14:12:08Z" | "2021-05-18T12:48:41Z" | MEMBER | null | perf neutral refactor, another step towards #41372
next steps
move code in `str_extract` into `extract` into StringMethods
combine code in `_str_extract_noexpand` and `_str_extract_expand` into a `_str_extract` method on the array
(not done here to keep diff simpler to review) | {
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Wrong graph below
Correct graph
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} | 1 | "2021-05-18T12:37:18Z" | "2021-05-18T13:34:54Z" | "2021-05-18T13:34:42Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
There is https://github.com/pandas-dev/pandas/issues/37277 which is about the same error message but which isn't about the fact that this shows up in a common operation like joining. Maybe there is a way to make the join work without fixing the underlying issue?
- [ ] I have confirmed this bug exists on the latest version of pandas.
Unfortunately my work doesn't allow me to test this on latest pandas versions, but given the related issue above and given that I haven't found this issue reported anywhere, I would be surprised if this was fixed in the meantime. I apologize if it was.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(
np.random.normal(size=[4,1]),
index=pd.MultiIndex.from_product([['a', 'b'], [0.1, 0.2]]),
columns=['col0']
).rename_axis(index=['level0', 'level1'])
df
df2 = pd.DataFrame(
np.random.normal(size=[2,1]),
#index=pd.Index(['a', 'b']),
index=pd.MultiIndex.from_product([['a', 'b']]),
columns=['col1']
).rename_axis(index=['level0'])
display(df)
display(df2)
df.join(df2, on=['level0'])
```
#### Problem description
Throws an error
#### Expected Output
Throws no error. Uncomment the commented line above and comment the line below. This should be functionally equivalent.
PS: This probably doesn't belong here, but if I replace the `join` by a `merge` it works, but the `level1` of the MultiIndex disappears, making it useless for me.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.6.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17134
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None
pandas : 1.1.5
numpy : 1.17.4
</details>
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} | 1 | "2021-05-18T14:01:17Z" | "2021-05-18T18:16:28Z" | "2021-05-18T16:52:48Z" | MEMBER | null | first followup to #41484 | {
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} | 1 | "2021-05-18T14:52:52Z" | "2021-05-18T18:22:26Z" | "2021-05-18T16:51:50Z" | MEMBER | null | perf neutral refactor
combine `_str_extract_expand`, `_str_extract_noexpand` and `_groups_or_na_fun` into a single function `_str_extract` ready to move to array method. | {
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} | 2 | "2021-05-18T15:17:41Z" | "2021-05-21T17:19:49Z" | "2021-05-21T16:45:09Z" | MEMBER | null | ATM we set `self.grouper` in both _get_grouper and in _set_grouper, but to different types. This changes _set_grouper to set _gpr_index instead, which we can type as `Index | None` | {
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~~draft as conflict with #41539~~ | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
df = pd.DataFrame(
[1, 2, 3],
columns=['a'],
index=pd.date_range(
freq="1T",
start="2021-01-01 00:00:00",
end="2021-01-01 00:02:00"
)
)
df.to_parquet("test.parquet", engine="pyarrow")
pd.testing.assert_frame_equal(pd.read_parquet("test.parquet", engine="pyarrow"), df)
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../lib/python3.8/site-packages/pandas/_testing.py", line 1704, in assert_frame_equal
assert_series_equal(
File ".../python3.8/site-packages/pandas/_testing.py", line 1389, in assert_series_equal
assert lidx.freq == ridx.freq, (lidx.freq, ridx.freq)
AssertionError: (None, <Minute>)
```
#### Problem description
With the following versions:
- pandas: 1.2.4
- pyarrow: 4.0.0
The bug is different with fastparquet, but there seem to be also a consistency issue (the index name).
Which I didn't see in earlier versions of pandas/fastparquet (0.25.3/0.4.1), I didn't try other combinations.
EDIT: _It wasn't happening before most likely because assert_frame_equal wasn't checking the index frequency, not because it was there._
It seems that the index frequency is not passed in the pandas metadata within the table.
Maybe I'm missing a pyarrow option to make this happen, but I still feel like the index should be either discarded completely or kept in a compatible manner.
#### Expected Output
No exception raised and an index with the same attributes as the original one.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-72-generic
Version : #80~18.04.1-Ubuntu SMP Mon Apr 12 23:26:25 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.05.0
fastparquet : 0.6.3
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
This ideally should still return `datetime64[ns]` dtype
```
In [1]: df = DataFrame(columns=["a", "b"], dtype="datetime64[ns]")
In [2]: df
Out[2]:
Empty DataFrame
Columns: [a, b]
Index: []
In [3]: df.quantile(0.5, axis=1, numeric_only=False)
Out[3]: Series([], Name: 0.5, dtype: float64)
In [4]: df.quantile(0.5, axis=1, numeric_only=False).dtype
Out[4]: dtype('float64')
In [5]: df
Out[5]:
Empty DataFrame
Columns: [a, b]
Index: []
In [6]: df.T
Out[6]:
Empty DataFrame
Columns: []
Index: [a, b]
# Probably the reason why we might get float64 dtype afterwards
In [7]: df.T.dtypes
Out[7]: Series([], dtype: object)
```
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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] | closed | false | null | [] | null | 6 | "2021-05-18T17:44:33Z" | "2021-06-11T17:51:27Z" | "2021-06-11T17:51:26Z" | CONTRIBUTOR | null | - [ ] closes #40876
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
The FrameOrSeriesUnion check was already removed. I have removed the FrameOrSeriesUnion alias, and replaced all instances of FrameOrSeriesUnion with DataFrame | Series | {
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} | 3 | "2021-05-18T17:49:55Z" | "2021-05-18T22:23:50Z" | "2021-05-18T18:23:44Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df.to_excel("output.xlsx")
```
#### Problem description
While using the above code snippet in Python 3.9.5
```
Traceback (most recent call last):
File "/mnt/windowsD/Repos/Aster_TelegramBot/env/lib/python3.9/site-packages/telegram/ext/utils/promise.py", line 80, in run
self._result = self.pooled_function(*self.args, **self.kwargs)
File "/mnt/windowsD/Repos/Aster_TelegramBot/rate.py", line 98, in plot_rate
df.to_excel("output.xlsx")
File "/mnt/windowsD/Repos/Aster_TelegramBot/env/lib/python3.9/site-packages/pandas/core/generic.py", line 2189, in to_excel
formatter.write(
File "/mnt/windowsD/Repos/Aster_TelegramBot/env/lib/python3.9/site-packages/pandas/io/formats/excel.py", line 815, in write
writer = ExcelWriter( # type: ignore[abstract]
File "/mnt/windowsD/Repos/Aster_TelegramBot/env/lib/python3.9/site-packages/pandas/io/excel/_openpyxl.py", line 30, in __init__
from openpyxl.workbook import Workbook
ModuleNotFoundError: No module named 'openpyxl'
```
#### Note: Installing openpyxl fixes the error: ```pip install -U openpyxl```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.12.1-2-MANJARO
Version : #1 SMP PREEMPT Sun May 2 13:56:40 UTC 2021
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_IN
LOCALE : en_IN.ISO8859-1
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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```
before after ratio
[09f3bf80] [ec8f4b2d]
<master> <get_dummies>
+ 647±4ms 761±6ms 1.18 strings.Dummies.time_get_dummies('arrow_string')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
```
```
Timer unit: 1e-06 s
Total time: 0.778161 s
File: /home/simon/pandas/pandas/core/arrays/string_arrow.py
Function: _str_get_dummies at line 937
Line # Hits Time Per Hit % Time Line Contents
==============================================================
937 def _str_get_dummies(self, sep="|"):
938 1 2.0 2.0 0.0 if pa_version_under4p0:
939 return super()._str_get_dummies(sep)
940
941 1 12415.0 12415.0 1.6 result = pc.split_pattern(self._data, pattern=sep)
942 1 8084.0 8084.0 1.0 uniques = pc.list_flatten(result).unique()
943 1 143.0 143.0 0.0 tags = uniques.take(pc.array_sort_indices(uniques)).to_pylist()
944 1 53.0 53.0 0.0 dummies = np.empty((len(result), len(tags)), dtype=np.int64)
945 63 160.0 2.5 0.0 for i, tag in enumerate(tags):
946 62 20660.0 333.2 2.7 match_equal = pc.equal(self._data, tag)
947 62 186099.0 3001.6 23.9 match_mid = pc.match_substring(self._data, sep + tag + sep)
948 124 112610.0 908.1 14.5 match_start = pc.match_substring_regex(
949 62 346.0 5.6 0.0 self._data, "^" + tag + re.escape(sep)
950 )
951 62 383985.0 6193.3 49.3 match_end = pc.match_substring_regex(self._data, re.escape(sep) + tag + "$")
952 124 435.0 3.5 0.1 match = pc.or_(
953 62 1111.0 17.9 0.1 pc.or_(pc.or_(match_start, match_end), match_mid), match_equal
954 )
955 62 5989.0 96.6 0.8 casted = match.cast(pa.int64())
956 62 2072.0 33.4 0.3 filled = pc.fill_null(casted, 0)
957 62 43997.0 709.6 5.7 dummies[:, i] = filled
958 1 0.0 0.0 0.0 return dummies, tags
```
plenty more ideas to try. so parking here for now.
I think we should assume that in most cases the number of rows will be much greater then the number of dummies, but taking advantage of the split rows may be beneficial.
for now outer loop is dummies (tags).
match_substring is faster than match_substring_regex so if I can append a separator to the start and end efficiently in pyarrow land that should help.
match_equal is faster still, so maybe can operate on the pyarrow list array.
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- [x] I have confirmed this bug exists on the latest version of pandas. _(which is 1.2.4 at the moment)_
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Example data and intended output
Let's create some synthetic data to illustrate the issue:
```python
df = pd.DataFrame({
'household_id' : ['b80', 'b90', 'a70', 'c60', 'a50', 'X13', 'X12', 'X11', 'XYZ'],
'district' : 5*['Neverland'] + 4*['Narnia'],
'income' : [1200, 2000, 1000, 900, 1200, 1500, 800, 900, 850],
})
df = df.set_index('household_id')
```
```
district income
household_id
b80 Neverland 1200
b90 Neverland 2000
a70 Neverland 1000
c60 Neverland 900
a50 Neverland 1200
X13 Narnia 1500
X12 Narnia 800
X11 Narnia 900
XYZ Narnia 850
```
Suppose we want to add some money to the poorest households. We have 600$ per district to distribute to households and we decide that this money will be divided equally among all households in a district that have income 1000$ or below. So, our expected result is:
```
district income
household_id
b80 Neverland 1200
b90 Neverland 2000
a70 Neverland 1300
c60 Neverland 1200
a50 Neverland 1200
X13 Narnia 1500
X12 Narnia 1000
X11 Narnia 1100
XYZ Narnia 1050
```
#### Code sample and actual output
We will try to achieve the described goal by using `groupby` followed by `transform`:
```python
def add_income_to_poor_households(households):
money_to_share = 600
poor_households_ids = households.index[households<=1000]
paylist = pd.Series(money_to_share/len(poor_households_ids), index=poor_households_ids)
return households.add(paylist, fill_value=0)
grouped = df.groupby('district')['income']
df['income'] = grouped.transform(add_income_to_poor_households)
```
After executing the above code `df` looks like this:
```
district income
household_id
b80 Neverland 1200
b90 Neverland 1300
a70 Neverland 1200
c60 Neverland 2000
a50 Neverland 1200
X13 Narnia 1100
X12 Narnia 1000
X11 Narnia 1500
XYZ Narnia 1050
```
which doesn't match the expected output given above.
#### Problem description
The output obtained with the sample code doesn't match the expected output. It's unclear to me which of the assumptions specified in the documentation of `SeriesGrupBy.transform` are violated by the above code. I think that none, so I figure it's a bug.
To gain some insight into the roots of the problem, note that the series returned by `add_income_to_poor_households` are ordered differently than the original ones. Please execute this on the original `df` (before changing it via using `transform`):
```python
households = df[df['district']=='Neverland']['income']
```
Now `households` looks like this:
```
household_id
b80 1200
b90 2000
a70 1000
c60 900
a50 1200
Name: income, dtype: int64
```
But after calling `add_income_to_poor_households` the order of the index changes:
```python
add_income_to_poor_households(households)
```
```
household_id
a50 1200.0
a70 1300.0
b80 1200.0
b90 2000.0
c60 1200.0
dtype: float64
```
So possibly the `transform` method fails to align the output of the inner function `add_income_to_poor_households` to the original indices of the data frame subject to grouping.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-73-generic
Version : #82-Ubuntu SMP Wed Apr 14 17:39:42 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : pl_PL.UTF-8
LOCALE : pl_PL.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 1 | "2021-05-18T20:31:18Z" | "2021-05-27T13:18:21Z" | "2021-05-27T13:18:21Z" | CONTRIBUTOR | null | - [ ] xref #41485
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | "2021-05-18T21:12:29Z" | "2021-05-24T17:19:50Z" | "2021-05-24T17:19:50Z" | MEMBER | null | See this comment from a previous pull request I submitted: https://github.com/pandas-dev/pandas/pull/41524#discussion_r634344743
Just need to add `, optional`, so opening as good first issue | {
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 3 | "2021-05-19T00:59:21Z" | "2021-07-28T22:55:13Z" | "2021-07-28T22:55:13Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
In [2]: df = pd.DataFrame({'a': [2, 1, 2, 1], 'b': ['x', 'x', 'y', 'y']})
In [3]: df.groupby('a').shift(1)
Out[3]:
b
0 NaN
1 NaN
2 x
3 x
In [4]: df.groupby('a').shift(1, fill_value='fill')
Out[4]:
a b
0 fill fill
1 fill fill
2 2 x
3 1 x
```
#### Problem description
When specifying `fill_value` in `groupby.shift`, the returned result includes the key column with keys filled with `fill_value`. When `fill_value` is unspecified (None), the key column is not included.
#### Expected Output
It seems pretty strange that keys are to be filled with `fill_value`. This makes more sense to me:
```python
In [4]: df.groupby('a').shift(1, fill_value='fill')
Out[4]:
b
0 fill
1 fill
2 x
3 x
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.4
hypothesis : 6.12.0
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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I wish I could use pd.merge and keep the common columns just from either the left/right df instead of getting col_x/col_y.
#### Describe the solution you'd like
pd.merge should get a new parameter like - keep_common_columns_from - which can take three arguments -
1. only_left
2. only_right
3. both
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https://api.github.com/repos/pandas-dev/pandas/issues/41562 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41562/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41562/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41562/events | https://github.com/pandas-dev/pandas/issues/41562 | 895,116,948 | MDU6SXNzdWU4OTUxMTY5NDg= | 41,562 | QST: Pandas Dataframe Rolling based on timeframe throwing exception “window must be an integer” | {
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] | closed | false | null | [] | null | 2 | "2021-05-19T07:55:47Z" | "2021-08-20T05:39:02Z" | "2021-08-20T05:39:02Z" | NONE | null | This is the schema of the dataframe i'm using to evaluate aggregate based on rolling timeframe.
```
AssetId int64
TimeStampCol datetime64[ns]
paramName1 object
paramName2 object
paramName3 object
```
My requirement is that I want to find the min of paramName1 over the period of past 1 hour data.
I'm forming the eval expression dynamically which will be like
`streamingDf.rolling('1H').paramName1.min()`
```eval(f'streamingDf.rolling('1H').paramName1.min()') ```
then I'm evaluating the expression to get the result dataframe.
Pandas Dataframe ***rolling based on timeframe*** throwing exception
`window must be an integer`
I'm also explicitly setting a datetime column as the index of this dataframe, before evaluating this expression.
```
streamingDf['TimeStampCol'] = pd.to_datetime(streamingDf['TimeStampCol'])
streamingDf.set_index('TimeStampCol')
```
Can someone help to resolve this issue.
- [ ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
# Your code here, if applicable
```
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https://api.github.com/repos/pandas-dev/pandas/issues/41563 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41563/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41563/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41563/events | https://github.com/pandas-dev/pandas/issues/41563 | 895,127,627 | MDU6SXNzdWU4OTUxMjc2Mjc= | 41,563 | BUG: pd.date_range gives a error when freq = LastWeekOfMonth(1) | {
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] | open | false | null | [] | null | 0 | "2021-05-19T08:07:03Z" | "2021-08-20T05:43:02Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> from pandas.tseries.offsets import *
>>> fq = LastWeekOfMonth(1)
>>> pd.date_range('2018-04-01', periods=1, freq=fq)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/miniconda3/envs/test37/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py", line 1077, in date_range
**kwargs,
File "/home/miniconda3/envs/test37/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 416, in _generate_range
values = np.array([x.value for x in xdr], dtype=np.int64)
File "/home/miniconda3/envs/test37/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 416, in <listcomp>
values = np.array([x.value for x in xdr], dtype=np.int64)
File "/home/miniconda3/envs/test37/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 2438, in generate_range
end = start + (periods - 1) * offset
File "pandas/_libs/tslibs/offsets.pyx", line 459, in pandas._libs.tslibs.offsets.BaseOffset.__mul__
File "pandas/_libs/tslibs/offsets.pyx", line 455, in pandas._libs.tslibs.offsets.BaseOffset.__mul__
File "pandas/_libs/tslibs/offsets.pyx", line 2647, in pandas._libs.tslibs.offsets.LastWeekOfMonth.__init__
ValueError: N cannot be 0
```
#### Problem description
The output of date_range with period = 1, freq = LastWeekOfMonth should not raise a error, since other freq like WeekOfMonth gives a normal output with period = 1.
#### Expected Output
```DatetimeIndex(['2018-04-30'], dtype='datetime64[ns]', freq='LWOM-MON')```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-72-generic
Version : #80~18.04.1-Ubuntu SMP Mon Apr 12 23:26:25 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 1.2.4
numpy : 1.19.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | "2021-05-19T10:33:58Z" | "2021-06-17T17:53:44Z" | "2021-06-17T17:53:44Z" | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame(
[[1,2,3,4],[5,6,7,8]],
columns=pd.MultiIndex.from_product(
[[0,1], ['foo','bar']],
names=['x', 'y']
)
)
after_groupby = df.groupby(df.index).ffill()
print(f"original dataframe:\n{df}\n")
print(f"result of groupby().ffill():\n{after_groupby}\n")
print(f"original dataframe columns.names:\n{df.columns.names}\n")
print(f"columns.names after groupby().ffill():\n{after_groupby.columns.names}\n")
```
#### Actual output
```
original dataframe:
x 0 1
y foo bar foo bar
0 1 2 3 4
1 5 6 7 8
result of groupby().ffill():
0 1
foo bar foo bar
0 1 2 3 4
1 5 6 7 8
original dataframe columns.names:
['x', 'y']
columns.names after groupby().ffill():
[None, None]
```
#### Expected output
```
original dataframe:
x 0 1
y foo bar foo bar
0 1 2 3 4
1 5 6 7 8
result of groupby().ffill():
x 0 1
y foo bar foo bar
0 1 2 3 4
1 5 6 7 8
original dataframe columns.names:
['x', 'y']
columns.names after groupby().ffill():
['x', 'y']
```
#### Problem description
Calling `groupby().ffill()` on a DataFrame with a `MultiIndex` column index strips the `names` attribute of the column index. The above shows the trivial case of `groupby(df.index)`, but I have also found it to occur with nontrivial `by` arguments (e.g. grouping a `DatetimeIndex` by dates).
This is obviously unexpected and undesirable.
#### Output of ``pd.show_versions()``
I have observed this bug on WSL with Python 3.6 and pandas 1.1.5, as well as an M1 MacBook Air with the latest available M1-native pandas version from `conda`. The details of the latter are below.
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:06:51 PST 2021; root:xnu-7195.81.3~1/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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} | 3 | "2021-05-19T11:17:37Z" | "2021-05-20T15:22:55Z" | null | NONE | null | #### Is your feature request related to a problem?
When trying to interpolate a column with dtype integer with `method="linear"` the error message is not very descriptive (example to reproduce at the bottom of this issue):
```
ValueError: Invalid fill method. Expecting pad (ffill) or backfill (bfill). Got linear
```
As "linear" is the default interpolation method I think the error message should be more descriptive, even though IntegerArray is still experimental according to the [docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html).
#### Describe the solution you'd like
I think the error message should include a hint to the potential cause that the array you're trying to interpolate is of dtype integer. Something like this could work:
```
ValueError: Invalid fill method. Expecting pad (ffill) or backfill (bfill). Got linear. Are you trying to interpolate an integer column?
```
I would be happy to create a PR with the improvement myself.
#### API breaking implications
As this only requires a change to the error message it does not break any API.
#### Describe alternatives you've considered
An alternative solution would be to implement linear interpolation for integer arrays but I suspect that that is not compatible/unexpected with some use-cases of integer arrays.
#### Additional context
Reproducable example:
```python
import pandas as pd
s = pd.Series([1, None, 3], dtype=pd.Int64Dtype())
s.interpolate(method="linear")
```
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} | 0 | "2021-05-19T11:34:39Z" | "2021-05-22T06:28:39Z" | "2021-05-21T15:18:29Z" | CONTRIBUTOR | null | came up in a comment to another PR, so here it is changed for all appropriate methods.
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} | 0 | "2021-05-19T12:07:18Z" | "2021-05-21T16:55:02Z" | "2021-05-21T16:47:43Z" | MEMBER | null | The padding of np.nan across all columns is not needed for StringArray/ArrowStringArray
```
before after ratio
[896256ee] [1c883e0d]
<master> <bypass>
- 99.3±0.6ms 87.4±1ms 0.88 strings.Split.time_split('string', True)
- 89.6±0.3ms 78.7±1ms 0.88 strings.Split.time_rsplit('arrow_string', True)
- 71.3±0.5ms 58.7±0.8ms 0.82 strings.Split.time_rsplit('string', True)
- 60.0±0.4ms 49.1±1ms 0.82 strings.Methods.time_partition('arrow_string')
- 59.0±0.7ms 47.5±1ms 0.80 strings.Methods.time_rpartition('arrow_string')
- 50.5±0.08ms 38.3±0.4ms 0.76 strings.Methods.time_partition('string')
- 49.5±0.3ms 37.2±0.2ms 0.75 strings.Methods.time_rpartition('string')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
this is a small change for perf.
The padding code logic does not work for st.extract since the first value of a list maybe np.nan followed by other values. I made that change here and then reverted as is moreless perf neutral. so will do in another PR that uses _wrap_result more for str_extract. (str.extract returns list of lists where any value in the list could be np.nan, str.split returns array of lists with different lengths without np.nans and the np.nans returned as scalars)
some further gains can also be achieved by bypassing the converting of `np.nan` to `[np.nan]` for partition (for StringArray/ArrowStringArray) since the DataFrame constructor does not need this for a list of tuples. (str.partition returns array of tuples, str.split returns array of lists) #41507 | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
pd.options.mode.use_inf_as_na = False
pd.Series([np.nan]).isnull()
pd.Series([np.nan]).isna()
pd.options.mode.use_inf_as_na = True
pd.Series([np.nan]).isnull()
pd.Series([np.nan]).isna()
```
Current Output:
```
0 False
dtype: bool
Out[205]:
0 False
dtype: bool
Out[205]:
0 True
dtype: bool
Out[205]:
0 True
dtype: bool
```
#### Problem description
I'm not able to identify NA values created by np.nan or generated by pandas through it's data manipulation methods using df.isna or df.isnull. I had to change `pd.options.mode.use_inf_as_na` from default False to True to recognize np.nan.
On my previous experiences with earlier versions of pandas the 2 methods can identify np.nan as NA. Now they mismatch what the docs are saying (https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html), which is by default with `pd.options.mode.use_inf_as_na = False`, np.nan should be recognized as NA, and setting this option to True only adds -INF and INF as NA.
What does the "old way" and "new way" in the statement from docs below mean anyway?
**True means treat None, NaN, -INF, INF as NA (old way), False means None and NaN are null, but INF, -INF are not NA (new way).**
#### Expected Output
```
0 True
dtype: bool
Out[205]:
0 True
dtype: bool
Out[205]:
0 True
dtype: bool
Out[205]:
0 True
dtype: bool
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.4.0
Version : Darwin Kernel Version 20.4.0: Fri Mar 5 01:14:02 PST 2021; root:xnu-7195.101.1~3/RELEASE_ARM64_T8101
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.18.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 56.0.0
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.17.0
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 3 | "2021-05-19T14:51:28Z" | "2021-05-19T15:51:55Z" | "2021-05-19T15:48:05Z" | NONE | null | I installed s3fs to try it out. I have removed it from my environment after deciding against it. However, someone upstream uses Pandas. Despite s3fs having been removed and no s3fs imports existing anywhere in the entire code base, Pandas wants to use s3fs. This causes my application to fail. Is there a way to correct this without having to recreate my entire development environment?
```
File "pandas\io\parsers.py", line 610, in read_csv
File "pandas\io\parsers.py", line 462, in _read
File "pandas\io\parsers.py", line 819, in __init__
File "pandas\io\parsers.py", line 1050, in _make_engine
File "pandas\io\parsers.py", line 1867, in __init__
File "pandas\io\parsers.py", line 1362, in _open_handles
File "pandas\io\common.py", line 558, in get_handle
File "pandas\io\common.py", line 333, in _get_filepath_or_buffer
File "fsspec\core.py", line 428, in open
File "fsspec\core.py", line 280, in open_files
File "fsspec\core.py", line 598, in get_fs_token_paths
File "fsspec\registry.py", line 213, in get_filesystem_class
ImportError: Install s3fs to access S3
``` | {
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] | open | false | null | [] | null | 3 | "2021-05-19T16:37:02Z" | "2021-05-21T18:05:30Z" | null | NONE | null | - [x ] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Pandas's dtype `Int8Dtype` is converted to `object` when melted.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({"a": pd.Series([1, 2], dtype=pd.Int8Dtype())})
melted = df.melt()
print(melted.dtypes)
```
#### Problem description
When melted, the Int8 column is casted to object type.
#### Expected Output
`melted.dtypes` should return:
```python
variable object
value Int8
dtype: object
```
#### Output of ``pd.show_versions()
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.0.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-143-generic
Version : #147-Ubuntu SMP Wed Apr 14 16:10:11 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.1.2
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : 1.0.8
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.3.22
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 2.0.1
xlwt : None
numba : 0.53.1
</details>
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] | open | false | null | [] | null | 0 | "2021-05-19T17:01:49Z" | "2021-08-20T05:44:47Z" | null | NONE | null | #### Is your feature request related to a problem?
When using `.loc` to index into a dataframe whose index is not sorted, a `KeyError` is raised that indicates the key does not exist. But sorting the index allows the same `.loc` command to pull a range of values from the dataframe.
#### Describe the solution you'd like
I'd like to see [this](https://github.com/pandas-dev/pandas/issues/11897) extended to regular indices. So that when I try to index into an unsorted dataframe, the error is more informative in how to fix it.
#### API breaking implications
Since `UnsortedIndexError` is already implemented, and inherits from `KeyError` I don't see any breaking implications?
#### Describe alternatives you've considered
Seems that bringing the `UnsortedIndexError` to regular indices (as it is already raised for a Multi-Index) is the best idea?
#### Additional context
I tested this with 1.2.4
```python
>>> df = pd.DataFrame({'val': ['a', 'b', 'c','d']}, index=[1.0, 3.0, 2.0, 4.0])
>>> df.loc[1.5:3.5]
Traceback (most recent call last):
File "/Users/JBEYER/opt/miniconda3/envs/env/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3080, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 975, in pandas._libs.hashtable.Float64HashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 982, in pandas._libs.hashtable.Float64HashTable.get_item
KeyError: 1.5
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/JBEYER/opt/miniconda3/envs/env/lib/python3.8/site-packages/pandas/core/indexing.py", line 895, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "/Users/JBEYER/opt/miniconda3/envs/env/lib/python3.8/site-packages/pandas/core/indexing.py", line 1102, in _getitem_axis
return self._get_slice_axis(key, axis=axis)
File "/Users/JBEYER/opt/miniconda3/envs/env/lib/python3.8/site-packages/pandas/core/indexing.py", line 1136, in _get_slice_axis
indexer = labels.slice_indexer(
File "/Users/JBEYER/opt/miniconda3/envs/env/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 5277, in slice_indexer
start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind)
File "/Users/JBEYER/opt/miniconda3/envs/env/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 5476, in slice_locs
start_slice = self.get_slice_bound(start, "left", kind)
File "/Users/JBEYER/opt/miniconda3/envs/env/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 5396, in get_slice_bound
raise err
File "/Users/JBEYER/opt/miniconda3/envs/env/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 5390, in get_slice_bound
slc = self.get_loc(label)
File "/Users/JBEYER/opt/miniconda3/envs/env/lib/python3.8/site-packages/pandas/core/indexes/numeric.py", line 395, in get_loc
return super().get_loc(key, method=method, tolerance=tolerance)
File "/Users/JBEYER/opt/miniconda3/envs/env/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3082, in get_loc
raise KeyError(key) from err
KeyError: 1.5
>>> df.sort_index().loc[1.5:3.5]
val
2.0 c
3.0 b
```
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Possibly related to:
- #28283
- #35425
#### Code Sample, a copy-pastable example
```python
df = pandas.DataFrame({"a": [1]})
df.a.attrs = {"test": "test"}
df2 = df.copy() # same behavior for df.reset_index()
```
```python
df2.a.attrs
```
```
{}
```
```python
df.a.attrs
```
```
{}
```
#### Problem description
Ideally, when making a copy, the attributes will be preserved on both dataframes.
#### Expected Output
```python
df2.a.attrs
```
```
{"test": "test"}
```
```python
df.a.attrs
```
```
{"test": "test"}
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 1963b810b3613baefe4b869bb9d034cfc9a027ef
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-73-generic
Version : #82-Ubuntu SMP Wed Apr 14 17:39:42 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+1660.g1963b810b3
numpy : 1.20.1
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.1.0
Cython : 0.29.23
pytest : 6.2.2
hypothesis : 6.13.0
sphinx : 3.5.4
blosc : 1.10.2
feather : None
xlsxwriter : 1.4.3
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.05.0
fastparquet : 0.6.3
gcsfs : 2021.05.0
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : 2021.05.0
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.17.1.dev149+g49aa235f.d20210518
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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] | closed | false | null | [] | null | 7 | "2021-05-19T19:41:53Z" | "2021-08-17T01:53:25Z" | "2021-08-17T01:53:25Z" | NONE | null | This PR allows for non-UTC printing of datetime strings from the `to_json` method when passing in local timestamps
- [x] closes #12997
- [x] tests modified
- [ ] Ensure all linting tests pass, see here for how to run them
- [x] whatsnew entry
Ok, so this is a doozy AND it's not done, but it can be merged as is.
What do we do:
We add timezones to json output!
Before:
```
ts = pd.Timestamp('2020-10-5 4:00:05', tz='US/Pacific')
print(pd.io.json.dumps(ts, iso_dates=True))
print(ts.isoformat())
```
would produce
`"2020-10-05T11:00:05.000Z"`
and
`"2020-10-05T04:00:05.000-07:00"`
respectively.
Now they both produce
`"2020-10-05T04:00:05.000-07:00"`
So what do I mean by not complete?
This still doesn't work for arrays that have datetime values. This only works for single datetime values.
So:
`pd.DataFrame({'foo': range(5)}, index=pd.date_range('2020-10-12', freq='H', periods=5, tz='US/Eastern')).to_json(date_format='iso')`
will still produce:
`'{"foo":{"2020-10-12T04:00:00.000Z":0,"2020-10-12T05:00:00.000Z":1,"2020-10-12T06:00:00.000Z":2,"2020-10-12T07:00:00.000Z":3,"2020-10-12T08:00:00.000Z":4}}'`
and not
`'{"foo":{"2020-10-12T00:00:00.000-04:00":0,"2020-10-12T01:00:00.000-04:00":1,"2020-10-12T02:00:00.000-04:00":2,"2020-10-12T03:00:00.000-04:00":3,"2020-10-12T04:00:00.000-04:00":4}}'`
Working on it currently. Open to recommendations! | {
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} | 5 | "2021-05-19T19:49:52Z" | "2021-05-25T17:34:31Z" | null | NONE | null | I have a large csv file with 15k columns with non-obvious names, of which 14990 are floating point numbers. I'd like to load them as floats without `read_csv` having to spend the time divining their types.
`dtype` allows providing a `dict`, but making one with all the column names is tedious and not always possible. The obvious solution is to provide a `defaultdict`, with a default of `np.float32`, and including entries for the other column types. Unfortunately currently, the default is silently ignored by `read_csv`. Presumably `read_csv` is not directly querying the dictionary, but rather checking first whether an item is there.
If this is not possible, it would be helpful to include a warning to the user, or at least some mention in the documentation, that defaultdict is not supported. It took me a long time to figure out why my floats weren't being treated as float32 and why read_csv was still trying to determine the types of these columns. | {
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] | open | false | null | [] | null | 0 | "2021-05-19T20:08:55Z" | "2021-05-31T21:57:49Z" | null | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame(columns=['A', 'B', 'C'])
In [3]: df.groupby('A').B.describe()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-7259c86156be> in <module>
----> 1 df.groupby('A').B.describe()
~/.../python3.8/site-packages/pandas/core/groupby/generic.py in describe(self, **kwargs)
674 if self.axis == 1:
675 return result.T
--> 676 return result.unstack()
677
678 def value_counts(
~/.../python3.8/site-packages/pandas/core/series.py in unstack(self, level, fill_value)
3827 from pandas.core.reshape.reshape import unstack
3828
-> 3829 return unstack(self, level, fill_value)
3830
3831 # ----------------------------------------------------------------------
~/.../python3.8/site-packages/pandas/core/reshape/reshape.py in unstack(obj, level, fill_value)
422 # Give nicer error messages when unstack a Series whose
423 # Index is not a MultiIndex.
--> 424 raise ValueError(
425 f"index must be a MultiIndex to unstack, {type(obj.index)} was passed"
426 )
ValueError: index must be a MultiIndex to unstack, <class 'pandas.core.indexes.base.Index'> was passed
In [4]: df.groupby('A').describe()
Out[4]: Series([], dtype: float64)
```
#### Problem description
`SeriesGroupBy.describe` raises an error when called on an empty dataset, and `DataframeGroupBy.describe` succeeds, but returns an empty `Series`.
#### Expected Output
I would expect both of these to return an empty `DataFrame` with the appropriate columns.
```python
In [3]: df.groupby('A').B.describe()
Out [3]:
Empty DataFrame
Columns: [count, mean, std, min, 25%, 50%, 75%, max]
Index: []
In [4]: df.groupby('A').describe()
Out [4]:
Empty DataFrame
Columns: [(B, count), (B, mean), (B, std), (B, min), (B, 25%), (B, 50%), (B, 75%), (B, max)(C, count), (C, mean), (C, std), (C, min), (C, 25%), (C, 50%), (C, 75%), (C, max)]
Index: []
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.26-1rodete1-amd64
Version : #1 SMP Debian 5.10.26-1rodete1 (2021-04-12)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : 0.29.13
pytest : 4.6.11
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | "2021-05-20T00:13:36Z" | "2021-05-24T18:42:05Z" | "2021-05-24T18:41:53Z" | MEMBER | null | numpy: #40851
sqlalchemy: #38344.
pytest: #40656
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} | 1 | "2021-05-20T00:29:31Z" | "2021-05-24T17:20:47Z" | "2021-05-24T17:19:50Z" | CONTRIBUTOR | null | - [ ] closes #41553
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 0 | "2021-05-20T01:06:50Z" | "2021-05-21T15:54:04Z" | "2021-05-21T15:45:13Z" | MEMBER | null | - [x] closes #26919
- [x] tests added / passed
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} | 1 | "2021-05-20T03:23:38Z" | "2021-05-24T19:02:59Z" | "2021-05-24T18:20:49Z" | MEMBER | null | - [x] closes #30173
- [x] tests added / passed
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} | 5 | "2021-05-20T07:46:31Z" | "2021-05-27T15:36:06Z" | "2021-05-27T14:43:51Z" | CONTRIBUTOR | null | - [x] xref #41485
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] | open | false | null | [] | null | 1 | "2021-05-20T11:19:44Z" | "2021-08-20T05:46:58Z" | null | NONE | null | Hey, I am using the linux environment on the raspberry pi 4 device and trying to install the pandas. I had ran almost these following commands one by one but none of them worked for me.
Command 1:
`sudo apt-get install python-pandas`
Command 2:
`python -m pip install pandas --upgrade`
Command-3:
`sudo pip3 install pandas`
It always throw the error and I am getting this following error https://gist.github.com/jaskiratsingh2000/a05789458a2cca2594cb947e414cd4d1
Please help me out in this. Thanks!
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({'A': [1, [], 3],'B': [4, 5, '']})
df == '' # element bool check work well with empty str
df == [] # want to find empty list
# raise ValueError(
# ValueError: Unable to coerce to Series, length must be 2: given 0
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
It is a very common requirement to check whether the elements in the DataFrame are empty list.
Pandas.DataFrame's "element bool check" works well with empty str '', as illustrated. But did not work with empyt list.
Although user could use : df.astype(bool), this method could NOT tell whether 'False' is a empty list or a empty str.
#### Expected Output
df == [] # want to find empty list
# Expected Output as follows:
‘’‘
A B
0 False False
1 True False
2 False False
’‘’
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Chinese (Simplified)_China.936
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 56.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/41584 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41584/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41584/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41584/events | https://github.com/pandas-dev/pandas/issues/41584 | 896,951,703 | MDU6SXNzdWU4OTY5NTE3MDM= | 41,584 | BUG: GroupBy.transform() unexpectedly coerces columns to object dtype when there is another object dtype column | {
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] | open | false | null | [] | null | 2 | "2021-05-20T15:04:22Z" | "2021-05-21T15:51:52Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({
'x': ['a', 'b', 'c', 'd'],
'y': [5, 6, 7, 8],
'g': [1, 2, 3, 3]
})
def myfirst(c):
return c.iloc[0]
# I'd expect the y col dtype to remain int64 in all examples below, but it changes to object
# when using myfirst() with transform() and there is at least one object column in the result.
df.groupby('g').transform('first').dtypes # int64 dtype when using builtin
df.groupby('g').transform(myfirst).dtypes # object dtype when using function
df.groupby('g').agg(myfirst).dtypes # int64 dtype when using function with agg()
df.drop(columns=['x']).groupby('g').transform(myfirst).dtypes # int64 dtype if object column is removed
df.groupby('g').transform(lambda c: type(myfirst(c)))['y'] == np.int64 # Confirm myfirst() is returning int64 scalars
```
#### Problem description
The current `transform()` behavior is inconsistent with the behavior of other reducers and with `agg()`. Those fall in like with my expectation to not change datatypes unless the output would otherwise prevent it (i.e. a reducer adds NaNs). However, here:
```python
df.groupby('g').transform(myfirst).dtypes
```
the int64 y column is coerced into an object dtype:
```
x object
y object
dtype: object
```
The coercion also occurs when the y column is `float` or `bool`, but _not_ when it is `datetime64[ns]`.
#### Expected Output
```
x object
y int64
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.7.final.0
python-bits : 64
OS : Darwin
OS-release : 20.4.0
Version : Darwin Kernel Version 20.4.0: Thu Apr 22 21:46:47 PDT 2021; root:xnu-7195.101.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.4
setuptools : 49.2.1
Cython : 0.29.14
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : 1.3.23
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
kwargs = {"start": "2020-01-01", "tz": "CET", "freq": "1MS", "closed": "left"}
index_one_year = pd.date_range(end="2021-01-01", **kwargs).tz_convert("UTC")
df_one_year = pd.DataFrame(data={"full": [1] * len(index_one_year)}, index=index_one_year)
index_one_month_more = pd.date_range(end="2021-02-01", **kwargs).tz_convert("UTC")
df_one_month_more = pd.DataFrame(
data={"one_month_more": [1] * len(index_one_month_more)}, index=index_one_month_more
)
pd.concat([df_one_year, df_one_year], axis=1) # works
pd.concat([df_one_year, df_one_month_more], axis=1) # raise:
# ValueError: Inferred frequency M from passed values does not conform to passed frequency MS
```
#### Problem description
<details>
<summary><i>Visualize the two dataframes</i></summary>
```python
full
2019-12-31 23:00:00+00:00 1
2020-01-31 23:00:00+00:00 1
2020-02-29 23:00:00+00:00 1
2020-03-31 22:00:00+00:00 1
2020-04-30 22:00:00+00:00 1
2020-05-31 22:00:00+00:00 1
2020-06-30 22:00:00+00:00 1
2020-07-31 22:00:00+00:00 1
2020-08-31 22:00:00+00:00 1
2020-09-30 22:00:00+00:00 1
2020-10-31 23:00:00+00:00 1
2020-11-30 23:00:00+00:00 1
```
```python
one_month_more
2019-12-31 23:00:00+00:00 1
2020-01-31 23:00:00+00:00 1
2020-02-29 23:00:00+00:00 1
2020-03-31 22:00:00+00:00 1
2020-04-30 22:00:00+00:00 1
2020-05-31 22:00:00+00:00 1
2020-06-30 22:00:00+00:00 1
2020-07-31 22:00:00+00:00 1
2020-08-31 22:00:00+00:00 1
2020-09-30 22:00:00+00:00 1
2020-10-31 23:00:00+00:00 1
2020-11-30 23:00:00+00:00 1
2020-12-31 23:00:00+00:00 1
```
</details>
A workaround is to convert dataframes back to the timezone "CET" where the inferred_freq is the correct one.
Still, I would have expected no issues on this concatenation, even when the timezone is UTC.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Darwin
OS-release : 20.4.0
Version : Darwin Kernel Version 20.4.0: Thu Apr 22 21:46:47 PDT 2021; root:xnu-7195.101.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.22
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.23
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 9 | "2021-05-20T17:15:39Z" | "2021-07-01T16:21:20Z" | "2021-06-07T16:47:34Z" | MEMBER | null | - [x] closes #15146
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This in turn will allow us to remove DatetimeArray.freq and TimedeltaArray.freq, which is needed for #31218 | {
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[https://github.com/Jiezheng2018/pandas/blob/65bba0b599faac90ab063a1b93b8532cf2639893/pandas/tests/series/indexing/test_where.py#L143-L144](url)
`https://github.com/Jiezheng2018/pandas/blob/65bba0b599faac90ab063a1b93b8532cf2639893/pandas/tests/series/indexing/test_where.py#L143-L144`
Both are done now :)
> https://github.com/Jiezheng2018/pandas/blob/65bba0b599faac90ab063a1b93b8532cf2639893/pandas/tests/series/indexing/test_where.py#L143-L144
_Originally posted by @Jiezheng2018 in https://github.com/pandas-dev/pandas/pull/41523#discussion_r636379142_ | {
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} | 7 | "2021-05-20T19:05:39Z" | "2021-10-30T15:10:03Z" | "2021-10-30T15:10:03Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```py
import pandas as pd
index: pd.IntervalIndex = pd.IntervalIndex.from_arrays([1], [2])
reveal_type(index)
reveal_type(index.right)
index.right.array
```
pyright
```
info: Type of "index" is "IntervalIndex"
info: Type of "index.right" is "() -> Index"
error: Cannot access member "array" for type "function"
Member "array" is unknown (reportFunctionMemberAccess)
```
mypy
```
note: Revealed type is 'pandas.core.indexes.interval.IntervalIndex'
note: Revealed type is 'Any'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.13.0-36-generic
Version : #40~16.04.1-Ubuntu SMP Fri Feb 16 23:25:58 UTC 2018
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.05.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 4 | "2021-05-20T19:54:37Z" | "2021-05-29T09:14:16Z" | "2021-05-26T10:04:04Z" | CONTRIBUTOR | null | - [X] xref #41486
- [X] tests added / passed
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [X] whatsnew entry
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[ 50.00%] ··· strings.Methods.time_replace ok
[ 50.00%] ··· ============== ==========
dtype
-------------- ----------
str 20.6±0ms
string 16.3±0ms
arrow_string 1.83±0ms
============== ==========
```
happy to break up if easier to review. | {
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I found this weird behaviour when transforming a timezone aware datetime object into a pandas datetime using to_datetime. If I set the timezone before transforming I get an incorrect result. Here is a small code snippet on how I get this:
```
import pandas as pd
import datetime
import pytz
tz=pytz.timezone("US/Eastern")
year=2021
month=1
day=1
hour=0
minute=0
#Add timezone after to_datetime
t1=datetime.datetime(year=year,month=month,day=day,hour=hour,minute=minute)
t1=pd.to_datetime(t1).tz_localize(tz)
#Add timezone before to_datetime
t2=datetime.datetime(year=year,month=month,day=day,hour=hour,minute=minute,tzinfo=tz)
t2=pd.to_datetime(t2)
print(t1)
print(t2)
```
which gives me:
```
t1 = 2021-01-01 00:00:00-05:00
t2 = 2020-12-31 23:56:00-05:00
```
t1 is the intended output, but t2 gives an strange 4 minutes different. I tried this with different random timezones and dates, and in most of the cases t1!=t2. I did not found a consistent difference for different timezones and dates.
I am not sure if this is a bug, or if I am missing something obvious. I tried to find if this was a known issue, but could not find anything, hence decided to share this here.
I am using python 3.8.6, pandas 1.1.2 and pytz 2020.1
Thanks! | {
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} | 4 | "2021-05-20T20:31:53Z" | "2021-05-21T15:42:08Z" | "2021-05-21T15:26:59Z" | MEMBER | null | We end up calling _try_cast in 3 places instead of 5 | {
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} | 3 | "2021-05-20T21:28:47Z" | "2021-05-24T11:47:32Z" | "2021-05-24T09:07:27Z" | CONTRIBUTOR | null | I noticed that the docs for `pd.merge()` and `pd.merge_asof()` were requiring the first argument to be a `DataFrame`, but in fact, they can be a `Series`. This PR updates the docs and the types to fix that issue.
Didn't think a `whatsnew` entry was needed, and I skipped creating an issue, but will do so if required.
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} | 8 | "2021-05-20T22:34:51Z" | "2021-06-29T13:55:27Z" | "2021-06-29T12:26:09Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
~~The Python 3.10 CI is actually red, but the continue-on-error flag makes it so that the job is not failed. WIP because tests actually have to be fixed.~~
Added Xfails for some tests. Pretty much green except for #41935 | {
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} | 6 | "2021-05-20T22:52:58Z" | "2021-09-23T16:22:27Z" | "2021-09-23T16:22:27Z" | NONE | null | - [ x] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
pd.__version__
cols = list('abcdefghjkl')
df = pd.DataFrame(np.random.randint(0, 100, size=(1_000_000, len(cols))), columns=cols)
df_str = df.astype(str)
df_string = df.astype('string')
%timeit df_str.groupby('a')[cols[1:]].agg('last')
%timeit df_string.groupby('a')[cols[1:]].agg('last')
```
#### Problem description
Pandas 1.2.x is much slower (9x slower) than 1.1.5 in the groupby aggregation above when the columns are of `string` dtype. When the columns are of `object` dtype performance are comparable across the two pandas version.
#### Expected Output
In pandas 1.1.5 this groupby-aggregation is a bit faster with `string` dtype than with `object` dtype:
```python
%timeit df_str.groupby('a')[cols[1:]].agg('last')
680 ms ± 3.86 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit df_string.groupby('a')[cols[1:]].agg('last')
544 ms ± 3.88 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
Conversely, in pandas 1.2.4 the same groupby-aggregation is **7x slower** with `string` dtype than with `object` dtype:
```python
%timeit df_str.groupby('a')[cols[1:]].agg('last')
700 ms ± 7.54 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit df_string.groupby('a')[cols[1:]].agg('last')
4.93 s ± 104 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
I would expect comparable performance between pandas 1.1.5 and 1.2.4, instead we have a large performance regression in 1.2.4 when performing the groupby aggregation with string dtypes.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-17-generic
Version : #18-Ubuntu SMP Thu May 6 20:10:11 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 49.6.0.post20210108
Cython : 0.29.23
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.3.23
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 2 | "2021-05-20T22:55:46Z" | "2021-05-22T01:39:22Z" | "2021-05-22T01:24:08Z" | MEMBER | null | - [x] closes #28145
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-05-21T02:30:44Z" | "2021-07-08T16:08:08Z" | "2021-07-08T16:08:08Z" | MEMBER | null | Code in `groupby.generic.DataFrameGroupBy._transform_general`:
https://github.com/pandas-dev/pandas/blob/b3e335254f46a526ee3ce9bb757eac4011d9d1fe/pandas/core/groupby/generic.py#L1299-L1309
This is calling `_choose_path` for every group, which in turn calls both the slow_path and the fast_path to determine if the fast path can be used. Indeed, running the code (from #41584):
```
df = pd.DataFrame({
'x': ['a', 'b', 'c', 'd'],
'y': [5, 6, 7, 8],
'g': [1, 2, 3, 3]
})
def myfirst(c):
return c.iloc[0]
print(df.groupby('g').transform(myfirst))
```
shows `myfirst` gets called 9 times - 3 times with columns x, 3 times with column y, and three times with the DataFrame consisting of x and y.
Should we just be calling choose_path on the first group to determine which can be used?
cc @phofl @jbrockmendel
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 0 | "2021-05-21T12:05:44Z" | "2021-05-25T13:35:30Z" | "2021-05-25T13:35:30Z" | MEMBER | null | - [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> ser = pd.Series(["A.", "a.", "Ab", "ab", np.nan], dtype="object")
>>> ser
0 A.
1 a.
2 Ab
3 ab
4 NaN
dtype: object
>>>
>>> ser.str.replace("a", "c", case=False, regex=False)
0 A.
1 c.
2 Ab
3 cb
4 NaN
dtype: object
>>>
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
```
>>> ser.str.replace("a", "c", case=False, regex=False)
0 c.
1 c.
2 cb
3 cb
4 NaN
dtype: object
>>>
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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} | 1 | "2021-05-21T13:12:32Z" | "2021-05-21T15:19:32Z" | "2021-05-21T15:11:15Z" | MEMBER | null | pre-cursor to #41590 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41604 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41604/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41604/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41604/events | https://github.com/pandas-dev/pandas/issues/41604 | 898,107,773 | MDU6SXNzdWU4OTgxMDc3NzM= | 41,604 | QST: | (or) behavior when working with two series that have different indexes | {
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} | 1 | "2021-05-21T14:37:00Z" | "2021-05-21T16:32:57Z" | null | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
When using the | operation between two dataframes, the order of the operation can give different results if you have nan, or different index.
```python
df_1 = pd.DataFrame([None], index=[1])
df_2 = pd.DataFrame([True], index=[1])
print(df_1 | df_2)
print(df_2 | df_1)
```
will give you different results:
```python
0
1 False
0
1 True
```
The same things with different index:
```python
df_1 = pd.DataFrame([True], index=[1])
df_2 = pd.DataFrame([True], index=[2])
print(df_1 | df_2)
print(df_2 | df_1)
```
will return
```python
0
1 True
2 False
0
1 False
2 True
```
Is this a normal behavior or a bug? If it is a normal behavior, what is the logic behind it?
Thanks! | {
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} | 1 | "2021-05-21T15:55:49Z" | "2021-05-21T18:44:13Z" | "2021-05-21T18:44:13Z" | NONE | null | When migrating from pandas 0.24 to 1.1.5 I stumbled across this:
```python
import pandas as pd
d1 = {'b':1,'a':2,'c':3}
d2 = {'a':1,'c':2,'b':3}
print(pd.DataFrame([d1, d2]))
# output from pandas 0.24.2:
# a b c
# 0 2 1 3
# 1 1 3 2
# output from pandas 1.1.5
# b a c
# 0 1 2 3
# 1 3 1 2
```
Both ran on python 3.7. Seems like old version used to always sort columns, while the new one doesn't and uses the order of the first item in the list. I didn't find any related information in docs or SO, so my question is: was this behaviour intended? Does this have anything to do with dictionaries preserving order (since python 3.6 I think)? | {
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} | 0 | "2021-05-21T16:01:19Z" | "2021-05-25T14:36:47Z" | "2021-05-25T13:35:30Z" | MEMBER | null | - [ ] closes #41602
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 3 | "2021-05-21T17:15:18Z" | "2021-05-24T19:30:17Z" | "2021-05-24T18:55:31Z" | MEMBER | null | - [x] closes #15452
- [x] closes #15613
- [x] closes #15829
- [x] closes #15891
- [x] closes #15898
- [x] closes #16018
- [x] closes #16174
- [x] closes #16309
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | "2021-05-21T19:58:53Z" | "2021-05-22T08:51:42Z" | "2021-05-22T00:24:57Z" | MEMBER | null | If we have
```python
import pandas as pd
reveal_type(pd.Series(range(3)).interpolate('linear'))
```
then on master we get:
```console
$ mypy t.py
t.py:3: note: Revealed type is 'Any'
```
while here
```console
$ mypy t.py
t.py:3: note: Revealed type is 'Union[pandas.core.series.Series, None]'
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#41555 was merged before i got a chance to push these suggested edits
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} | 0 | "2021-05-22T02:29:18Z" | "2021-11-17T02:11:51Z" | "2021-11-17T02:11:38Z" | CONTRIBUTOR | null | #### Is your feature request related to a problem?
It is often not clear which methods a "file-like object" needs to implement to be compatible with `read/to_*`.
#### Describe the solution you'd like
Define multiple `typing.Protocol`s to make clear which methods need to be implemented, similar to python/typeshed/issues/4212.
This will probably be a bit messy: 1) many protocols and 2) most libraries expect `typing.IO` (which will lead to a few ignore/cast statements but also might make it difficult to determine the set of compatible protocols).
edit:
If feasible, I would like that `pandas.io.common.get_handle` has roughly the following interface:
```py
IOProtocols = TypeVar("IOProtocols", "insert long(?) list of generic protocols (str/bytes)")
@overload
def get_handle(path_or_buf: os.PathLike, ...) -> IOHandles[IO[str]]: ...
@overload
def get_handle(path_or_buf: IOProtocols[AnyStr], ...) -> IOHandles[IOProtocols[str]]: ...
@overload
def get_handle(path_or_buf: os.PathLike, ..., *, is_text: Literal[False]) -> IOHandles[IO[bytes]]: ...
@overload
def get_handle(path_or_buf: IOProtocols[bytes], ..., *, is_text: Literal[False]) -> IOHandles[IOProtocols[bytes]]: ...
def get_handle(path_or_buf: IOProtocols | os.PathLike, ..., is_text: bool = True) -> IOHandles:
# implementation
``` | {
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} | 0 | "2021-05-22T04:07:38Z" | "2021-05-26T04:24:30Z" | "2021-05-26T01:49:26Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
A big partial-indexing test is split and has a non-trivial diff, but the only _actual_ change is this line https://github.com/pandas-dev/pandas/compare/master...jbrockmendel:ref-sanitize_column?expand=1#diff-1723bf927d192314f9d8c4faa220e26b1c3b39c2fe3bb65d8c43966ec7e596e8R430 declaring that empty range is int-dtype-like instead of float-dtype-like, which i think we should call a bug. | {
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} | 2 | "2021-05-22T05:29:06Z" | "2021-05-25T14:19:12Z" | "2021-05-25T08:14:34Z" | MEMBER | null | - [x] closes #38944
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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Possibly also adding to the `Styler` class description.
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https://api.github.com/repos/pandas-dev/pandas/issues/41614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41614/events | https://github.com/pandas-dev/pandas/issues/41614 | 898,733,357 | MDU6SXNzdWU4OTg3MzMzNTc= | 41,614 | DOC: use shared docstrings for repeated args in Styler methods | {
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- [ ] `subset` and `props` #44454 | {
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} | 2 | "2021-05-22T11:22:03Z" | "2021-06-03T23:31:22Z" | "2021-06-03T23:24:05Z" | CONTRIBUTOR | null | A few bits to make #41153 smaller.
Also fixes an issue with the current implementation of NumericIndex._engine_type:
The implementation currently uses `self.dtype.type`, which is a problem when we are given platform-specific dtypes, e.g. `np.intc`.
For example:
```python
>>> d1 = np.dtype(np.intc)
>>> d1
dtype('int32')
>>> d2 = np.dtype(np.int32)
>>> d1 == d2
True
>>> d1.type == d2.type
False # a problem
```
So the _engine_type dict should use np.dtype(np.int64) etc., so avoid such problems. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41616 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41616/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41616/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41616/events | https://github.com/pandas-dev/pandas/issues/41616 | 898,821,864 | MDU6SXNzdWU4OTg4MjE4NjQ= | 41,616 | BUG: very very slow when append long dictionary into HDF5 file | {
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] | open | false | null | [] | null | 1 | "2021-05-22T15:08:31Z" | "2021-08-21T01:29:52Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import numpy as np
import pandas as pd
from pandas import HDFStore,DataFrame# create (or open) an hdf5 file and opens in append mode
currenttimedata = {
"time": "20210101080808",
"desc": "Ford",
"status": "success",
"detail0": "somevalue",
"detail1": "somevalue",
"detail2": "somevalue",
"detail3": "somevalue",
"detail4": "somevalue",
"detail5": "somevalue",
"detail6": "somevalue",
"detail7": "somevalue",
"detail8": "somevalue",
"detail9": "somevalue",
"detail10": "somevalue",
"detail11": "somevalue",
"detail12": "somevalue",
"detail13": "somevalue",
"detail14": "somevalue",
"detail15": "somevalue",
"detail16": "somevalue",
"detail17": "somevalue",
"detail18": "somevalue",
"detail19": "somevalue",
"detail20": "somevalue",
"detail21": "somevalue",
"detail22": "somevalue",
"detail23": "somevalue",
"detail24": "somevalue",
"detail25": "somevalue",
"detail26": "somevalue",
"detail27": "somevalue",
"detail28": "somevalue",
"detail29": "somevalue",
"detail30": "somevalue",
"detail31": "somevalue",
"detail32": "somevalue",
"detail33": "somevalue",
"detail34": "somevalue",
"detail35": "somevalue",
"detail36": "somevalue",
"detail37": "somevalue",
"detail38": "somevalue",
"detail39": "somevalue",
"detail40": "somevalue",
"detail41": "somevalue",
"detail42": "somevalue",
"detail43": "somevalue",
"detail44": "somevalue",
"detail45": "somevalue",
"detail46": "somevalue",
"detail47": "somevalue",
"detail48": "somevalue",
"detail49": "somevalue",
"detail50": "somevalue",
}
hdf =HDFStore('storage.h5')
data = {}
for key, value in currenttimedata.items():
data[key] = [value]
print("data: ", data)
df =DataFrame(data, columns=list(currenttimedata.keys()))
print("df: ", df)
hdf.put('d1', df, format='table', data_columns=True)
print("hdf[d1] 1: ", hdf['d1'])
for x in range(100):
print("x: ", x)
hdf.append('d1', df, format='table', data_columns=True)
print("hdf[d1] 2: ", hdf['d1'])
hdf.close()# closes the file
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
**append very very slow!**
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
Version : #46-Ubuntu SMP Fri Jul 10 00:24:02 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 3.0.0
IPython : None
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import numpy as np
import pandas as pd
# test data: a numpy Datetime64
ts_np = np.Datetime64('2021-01-01T08:00:00.00')
# 1 hour offset: works as expected (1 day offset also works as expected)
do=pd.tseries.frequencies.to_offset('1h')
ts_np + do
```
Result
```bash
Timestamp('2021-01-01 09:00:00')
```
```python
# 1 week offset returns an error
do=pd.tseries.frequencies.to_offset('1W')
ts_np + do
```
Result
```bash
ts_np+do
Traceback (most recent call last):
File "<ipython-input-11-e20c02eb9ea8>", line 1, in <module>
ts_np+do
UFuncTypeError: ufunc 'add' cannot use operands with types dtype('<M8[ms]') and dtype('O')
```
```python
# 1 month begin offset returns an error
do=pd.tseries.frequencies.to_offset('1MS')
```
Result
```bash
ts_np+do
Traceback (most recent call last):
File "<ipython-input-13-e20c02eb9ea8>", line 1, in <module>
ts_np+do
UFuncTypeError: ufunc 'add' cannot use operands with types dtype('<M8[ms]') and dtype('O')
```
#### Problem description
Applying week or month offset on numpy `Datetime64` does not work.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-53-generic
Version : #60~20.04.1-Ubuntu SMP Thu May 6 09:52:46 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : 0.6.0
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2
</details>
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} | 4 | "2021-05-22T17:36:03Z" | "2021-05-27T21:23:16Z" | "2021-05-27T15:09:07Z" | CONTRIBUTOR | null | Change is made ahead of 1.3.0 to pre-empt the addition of `escape_latex` and to avoid name ambiguity.
This is not user facing since the `escape` kwarg was added for 1.3.0 only recently.
**Alternative**
If instead of having separate `escape_html` and `escape_latex` kwargs we could have `escape={None, 'html', 'latex'}` since it is likely they will be used exclusively?? | {
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